The availability of high-volume ‘omic’ data, including gene expression, metabolome, methylation, and microbiome, provides new opportunities to identify gene-environment (G×E) and omic × E interactions. This project will develop statistical methods to leverage omic data to improve power for identifying novel interactions as well as to inform the biological mechanism by which genes and exposures affect cancer outcomes.


William Gauderman, PhD

Professor of Population and Public Health Sciences